Overview:
TensorFlow is a 2nd Generation API of Google’s open source software library for Deep Learning. The system is designed to facilitate research in machine learning and to make it quick and easy to transition from research prototype to production system.
Pre-Requisite:
• Statistics
• Python
• (optional) A laptop with NVIDIA GPU that supports CUDA 8.0 and cuDNN 5.1, with 64-bit Linux installed
Audience:
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation/production environment/architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging
Course Curriculum
| Introduction to Data Science | |||
| Introduction to Data Science, Different Machine Learning Paradigms Details | 00:00:00 | ||
| Analytical Terminology, Analytical Methodology Details | 00:00:00 | ||
| Unsupervised Learning Algorithms Details | 00:00:00 | ||
| Supervised Learning Algorithms Details | 00:00:00 | ||
| Introduction to Python | |||
| Reading Data from External Files (.Txt, .Xls, .Csv) Details | 00:00:00 | ||
| Data Exploration with Python Details | 00:00:00 | ||
| Data Manipulation with Python (Handling Missing Values,Outliers) Details | 00:00:00 | ||
| Data Preparation, Normalization and Combining Data with Python Details | 00:00:00 | ||
| Introduction to TensorFlow | |||
| Installing TensorFlow on windows Details | 00:00:00 | ||
| Overview of TensorFlow Details | 00:00:00 | ||
| The Programming Model, Data Model Details | 00:00:00 | ||
| Tensor Board Details | 00:00:00 | ||
| Introduction to Neural Nets and Deep Learning | |||
| Fundamental concepts of Neural Nets Details | 00:00:00 | ||
| Limitations of ANN Details | 00:00:00 | ||
| Activation Functions, Optimization Techniques Details | 00:00:00 | ||
| Implementing SLP and MLP in TensorFlow Details | 00:00:00 | ||
| CNN | |||
| Introduction to CNN Details | 00:00:00 | ||
| CNN Architecture, Pooling Layer Details | 00:00:00 | ||
| Efficient Convolution Algorithms Details | 00:00:00 | ||
| Case study on CNN Details | 00:00:00 | ||
| Recurrent and Recursive Nets | |||
| Basic concepts of RNN Details | 00:00:00 | ||
| The Vanishing Gradient Problem Details | 00:00:00 | ||
| LSTM Networks, Recursive Neural Networks Details | 00:00:00 | ||
| Case study on RNN Details | 00:00:00 | ||
| Unsupervised Learning: Autoencoders, RBM | |||
| Introducing Autoencoders Details | 00:00:00 | ||
| Stochastic Encoders and Decoders Details | 00:00:00 | ||
| Restricted Boltzmann Machines Details | 00:00:00 | ||
| Case study Details | 00:00:00 | ||
| Reinforcement Learning | |||
| Introduction to Reinforcement Learning Details | 00:00:00 | ||
| Implementing Policy Gradients Details | 00:00:00 | ||
| Q-Learning Algorithm Details | 00:00:00 | ||
| Case study Details | 00:00:00 | ||
| Generative Adversarial Networks | |||
| Introduction to Generative Adversarial Networks Details | 00:00:00 | ||
| Understanding GANs Details | 00:00:00 | ||
| Implementing DCGAN Details | 00:00:00 | ||
| Up-scaling the resolution Details | 00:00:00 | ||
| Natural Language Processing | |||
| Introduction to NLP Details | 00:00:00 | ||
| Analyzing sentiment Details | 00:00:00 | ||
| Translating Sentences Details | 00:00:00 | ||
| Summarizing Text Details | 00:00:00 | ||
Course Reviews
No Reviews found for this course.